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Data from: Oscillatory signatures underlie growth regimes in Arabidopsis pollen tubes: computational methods to estimate tip location, periodicity and synchronization in growing cells

Citation

Damineli, Daniel S.C.; Portes, Maria Teresa; Feijó, José A. (2017), Data from: Oscillatory signatures underlie growth regimes in Arabidopsis pollen tubes: computational methods to estimate tip location, periodicity and synchronization in growing cells, Dryad, Dataset, https://doi.org/10.5061/dryad.6806c

Abstract

Oscillations in pollen tubes have been reported for many cellular processes, including growth, extracellular ion fluxes, and cytosolic ion concentrations. However, there is a shortage of quantitative methods to measure and characterize the different dynamic regimes observed. Herein, a suite of open-source computational methods and original algorithms were integrated into an automated analysis pipeline that we employed to characterize specific oscillatory signatures in pollen tubes of Arabidopsis thaliana (Col-0). Importantly, it enabled us to detect and quantify a Ca2+ spiking behaviour upon growth arrest and synchronized oscillations involving growth, extracellular H+ fluxes, and cytosolic Ca2+, providing the basis for novel hypotheses. Our computational approach includes a new tip detection method with subpixel resolution using linear regression, showing improved ability to detect oscillations when compared to currently available methods. We named this data analysis pipeline ‘Computational Heuristics for Understanding Kymographs and aNalysis of Oscillations Relying on Regression and Improved Statistics’, or CHUKNORRIS. It can integrate diverse data types (imaging, electrophysiology), extract quantitative and time-explicit estimates of oscillatory characteristics from isolated time series (period and amplitude) or pairs (phase relationships and delays), and evaluate their synchronization state. Here, its performance is tested with ratiometric and single channel kymographs, ion flux data, and growth rate analysis.

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Funding

National Science Foundation, Award: MCB-1616437/2016